Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Nat Commun. 2021 Sep 24;12(1):5647. doi: 10.1038/s41467-021-25805-y.


Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10-51 and rTG = 0.13, PTG = 1.1 × 10-68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Causality
  • Gene Expression Profiling / methods*
  • Genetic Association Studies / methods
  • Genetic Predisposition to Disease / genetics*
  • Genome-Wide Association Study / methods*
  • Humans
  • Mendelian Randomization Analysis / methods
  • Phenotype
  • Polymorphism, Single Nucleotide*
  • Quantitative Trait Loci / genetics
  • Transcriptome / genetics*